TY  - JOUR
AU  - Lian, Bin
AU  - Zhang, Haohui
AU  - Wang, Tao
AU  - Wang, Yongtian
AU  - Shang, Xuequn
AU  - Aziz, N Ahmad
AU  - Hu, Jialu
TI  - Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis.
JO  - Briefings in bioinformatics
VL  - 26
IS  - 4
SN  - 1467-5463
CY  - Oxford [u.a.]
PB  - Oxford University Press
M1  - DZNE-2025-00786
SP  - bbaf309
PY  - 2025
AB  - Gene regulation varies across different cell types and developmental stages, leading to distinct cellular roles across cellular populations. Investigating cell type-specific gene coexpression is therefore crucial for understanding gene functions and disease pathology. However, reconstructing gene coexpression networks from single-cell transcriptome data is challenging due to artifacts, noise, and data sparsity. Here, we present an efficient method for inference of gene coexpression networks via variance decomposition analysis (GCNVDA) to explore the underlying gene regulatory mechanisms from single-cell transcriptome data. Our model incorporates multiple sources of variability, including a random effect term G to capture gene-level variance and a random effect term E to account for residual errors. We applied GCNVDA to three real-world single-cell datasets, demonstrating that our method outperforms existing state-of-the-art algorithms in both sensitivity and specificity for identifying tissue- or state-specific gene regulations. Furthermore, GCNVDA facilitates the discovery of functional modules that play critical roles in key biological processes such as embryonic development. These findings provide new insights into cell-specific regulatory mechanisms and have the potential to significantly advance research in developmental biology and disease pathology.
KW  - Single-Cell Analysis: methods
KW  - Gene Regulatory Networks
KW  - Algorithms
KW  - Transcriptome
KW  - Gene Expression Profiling: methods
KW  - Humans
KW  - Computational Biology: methods
KW  - Animals
KW  - gene coexpression networks (Other)
KW  - gene functional modules (Other)
KW  - linear mixed model (Other)
KW  - single-cell RNA sequencing (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40618350
C2  - pmc:PMC12229094
DO  - DOI:10.1093/bib/bbaf309
UR  - https://pub.dzne.de/record/279459
ER  -